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I have the true output as the ratio between 0 and 1. I am trying predict the output using regression. I am supposed to find the area under the ROC curve between the prediction and true values. I am not aware of ROC curve calculations for continuous variables. Any suggestions are welcome.

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marked as duplicate by Sycorax, Michael Chernick, mdewey, kjetil b halvorsen, steffen Jul 31 '18 at 15:16

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  • $\begingroup$ What complicates the problem for continuous variables? $\endgroup$ – Michael Chernick Jul 7 '17 at 3:09
  • $\begingroup$ As Andreas Mueller said and I quote "roc_auc is a classification or ranking metric, not a regression metric. So it doesn't accept continuous y" - amueller here and here $\endgroup$ – ultramarine Jul 7 '17 at 3:13
  • $\begingroup$ You didn't make it clear what variable(s) are used in the regression to predict the ratio. The receiver operating characteristic curve shows how the probability of correct selection (in classification) varies as a threshold is varied. In regression you can predict the accuracy of the prediction. You could pick a threshold for the accuracy and declare the result to be successful if you achieve that accuracy. I suppose you could construct an ROC curve by varying the threshold. $\endgroup$ – Michael Chernick Jul 7 '17 at 3:32
  • $\begingroup$ I have both categorical and quantitative variables that are being used in regression. What do you mean by threshold? Should I threshold both the true output and the predicted output and vary that threshold to get an ROC curve? $\endgroup$ – ultramarine Jul 7 '17 at 3:44
  • $\begingroup$ No I am talking about the absolute difference between predicted and actual. $\endgroup$ – Michael Chernick Jul 7 '17 at 3:50